Policy analysts at the Independent Institute tell us that Robogeddon, the Day our robot bosses fire us all, is not imminent. The coming “death of jobs” is greatly exaggerated:

According to consultancy McKinsey, up to 33 percent of the U.S. workforce may need to change occupations and learn new skills by 2030 due to digitization, automation, and advances in artificial intelligence. In contrast, Oren Cass, senior fellow at the Manhattan Institute, argues that “all of the economic data suggest that jobs are being destroyed by automation slower than ever.” Regardless of the disruption, retraining is a better response, and the private sector retrains better than government. LAWRENCE MCQUILLAN AND REBECCA SKLAR, “The solution for ‘robogeddon’ is rapid retraining, not guaranteed income” at The Hill

That’s worth considering. But reduced labor force participation is hard to measure systematically. People may not be looking for work because of their age, health, non-work-related responsibilities such as child or elder care, retraining programs, or a lack of fit between their skills and opportunities. The big picture is still not the Death of Jobs.

None of this would surprise Jay Richards, author of The Human Advantage: The Future of American Work in an Age of Smart Machines: He sees our era as more of a retooling than a meltdown. But retooling does mean change, work, cost, and risk.

One change he emphasizes is the need to retrain every so often, to keep up with changing economic patterns: “Routine factory and office work is passing away. So is the hope of a single stable job with one employer, and an education completed at age twenty-two that will sustain a career for the next forty-five years.” (pp. 10–11) There will be many paths—not necessarily high-tech—to many new ways to make a living. He elaborates,

In this time of light-speed creative destruction, we must learn from tough breaks, job churn, and failure. We should plan to do several things during our career. Don’t imagine a career spread out over adulthood as either a single full-time job or even a series of such jobs. We should not assume that we can finish our education at age eighteen or twenty-one, and depend on that for the next forty-five years. What students don’t prepare for in their formal education, they’ll have to learn later, on the job or on their own time.

Most of the work of the future doesn’t exist yet, so you can’t specialize for it. Sure, tech skills are valuable, and many of them can be learned on the cheap (more on that in a bit). A degree in engineering, science, or business still promises a good rate of return. You won’t go hungry if you avoid too much student debt, get good grades at a good school, graduate with a BS in a high-demand field such as computer engineering, and move to where the jobs are.
But don’t imagine that a high-tech economy requires us all to become coding wizards, any more than being a NASCAR driver requires you to be a mechanical engineer. Instead, you should develop a suite of skills that allows you to adapt quickly. (pp. 84-85, The Human Advantage

Many click-friendly predictions exaggerate the pace of automation as well. Noah Smith tackles this problem at Bloomberg:

It’s important to note that machine learning hasn’t yet made its mark on the economy — to paraphrase economist Robert Solow, you can see the machine learning age everywhere but in the economic statistics. Employment levels have returned to healthy levels, and there’s no evidence that machines are taking many of our jobs yet…

The authors generally don’t envision a world of full automation, with machines replacing humans at every step of the production process. Instead, they see machine learning being deployed selectively at some nodes of the value chain where data is plentiful, leaving human judgment to focus on the rest. Though “judgment” is a fuzzy word, Agrawal et al. basically identify two cognitive tasks in which humans will beat intelligent algorithms for the foreseeable future — making predictions based on small data samples, and identifying what constitutes success and failure. Humans are still better at knowing what they want, and at modeling the underlying structure of how the world works .Noah Smith, “Artificial Intelligence Still Isn’t All That Smart” at Bloomberg

Part of the “underlying structure of how the world works” is the many other, sometimes unrelated, events that affect the overall picture. For example, increasing longevity is creating many jobs and even new disciplines.

Consider memory care, health care that helps aged seniors retain their cognitive abilities. That’s an expanding field that hardly existed thirty years ago. Doubtless, machine learning will feature in most programs, for example in monitoring/prompting. But retaining and strengthening human memory (hence, relationships) is not really a job for robots and not likely to become one.

Text Robogeddon and tell them to Pause.

See also: Jay Richards asks, can training for an AI future be trusted to bureaucrats? We hear so much about how the AI revolution gobbles industrial era jobs that we don’t notice the digital era jobs going unfilled.

Mind Matters features original news and analysis at the intersection of artificial and natural intelligence. Through articles and podcasts, it explores issues, challenges, and controversies relating to human and artificial intelligence from a perspective that values the unique capabilities of human beings. Mind Matters is published by the Walter Bradley Center for Natural and Artificial Intelligence.